CONTRAST Statement

CONTRAST'label' row-description <, …, row-description </ options>>;

where a row-description is defined as follows:

effect values <, …, effect values>

The CONTRAST statement provides a mechanism for obtaining customized hypothesis tests. It is similar to the CONTRAST statement
in PROC LOGISTIC and PROC GLM, depending on the coding schemes used with any classification variables involved.

The CONTRAST statement enables you to specify a matrix, , for testing the hypothesis , where is the parameter vector. You must be familiar with the details of the model parameterization that PROC SURVEYLOGISTIC uses
(for more information, see the PARAM=
option in the section CLASS Statement). Optionally, the CONTRAST statement enables you to estimate each row, , of and test the hypothesis . For more information, see the section Testing Linear Hypotheses about the Regression Coefficients.

There is no limit to the number of CONTRAST statements that you can specify, but they must appear after the MODEL
statement.

The following parameters can be specified in the CONTRAST statement:

label

identifies the contrast on the output. A label is required for every contrast specified, and it must be enclosed in quotes.

effect

identifies an effect that appears in the MODEL statement. The name INTERCEPT can be used as an effect when one or more intercepts
are included in the model. You do not need to include all effects that are included in the MODEL statement.

values

are constants that are elements of the matrix associated with the effect. To correctly specify your contrast, it is crucial to know the ordering of parameters within
each effect and the variable levels associated with any parameter. The "Class Level Information" table shows the ordering
of levels within variables. The E option, described later in this section, enables you to verify the proper correspondence
of values to parameters.

The rows of are specified in order and are separated by commas. Multiple degree-of-freedom hypotheses can be tested by specifying multiple
row-descriptions. For any of the full-rank parameterizations, if an effect is not specified in the CONTRAST statement, all of its coefficients
in the matrix are set to 0. If too many values are specified for an effect, the extra ones are ignored. If too few values are specified,
the remaining ones are set to 0.

When you use effect coding (by default or by specifying PARAM=EFFECT
in the CLASS statement), all parameters are directly estimable (involve no other parameters).

For example, suppose an effect that is coded CLASS variable A has four levels. Then there are three parameters () that represent the first three levels, and the fourth parameter is represented by

To test the first versus the fourth level of A, you would test

or, equivalently,

which, in the form , is

Therefore, you would use the following CONTRAST statement:

contrast '1 vs. 4' A 2 1 1;

To contrast the third level with the average of the first two levels, you would test

When you use the less-than-full-rank parameterization (by specifying PARAM=GLM in the CLASS statement), each row is checked
for estimability. If PROC SURVEYLOGISTIC finds a contrast to be nonestimable, it displays missing values in corresponding
rows in the results. PROC SURVEYLOGISTIC handles missing level combinations of classification variables in the same manner
as PROC LOGISTIC. Parameters corresponding to missing level combinations are not included in the model. This convention can
affect the way in which you specify the matrix in your CONTRAST statement. If the elements of are not specified for an effect that contains a specified effect, then the elements of the specified effect are distributed
over the levels of the higher-order effect just as the LOGISTIC procedure does for its CONTRAST and ESTIMATE statements. For
example, suppose that the model contains effects A and B and their interaction A*B. If you specify a CONTRAST statement involving
A alone, the matrix contains nonzero terms for both A and A*B, since A*B contains A.

The degrees of freedom is the number of linearly independent constraints implied by the CONTRAST statement—that is, the rank
of .

You can specify the following options after a slash (/):

ALPHA=value

sets the confidence level for confidence intervals. The value of the ALPHA= option must be between 0 and 1, and the default value is 0.05. A confidence level of produces % confidence intervals. The default of ALPHA=0.05 produces 95% confidence intervals.

E

requests that the matrix be displayed.

ESTIMATE=keyword

requests that each individual contrast (that is, each row, , of ) or exponentiated contrast () be estimated and tested. PROC SURVEYLOGISTIC displays the point estimate, its standard error, a t or Wald confidence interval, and a t or Wald chi-square test for each contrast. The significance level of the confidence interval is controlled by the ALPHA=
option. You can estimate the contrast or the exponentiated contrast (), or both, by specifying one of the following keywords:

PARM

specifies that the contrast itself be estimated

EXP

specifies that the exponentiated contrast be estimated

BOTH

specifies that both the contrast and the exponentiated contrast be estimated

SINGULAR=value

tunes the estimability checking. If is a vector, define ABS() to be the largest absolute value of the elements of . For a row vector of the matrix , define

If ABS() is greater than c*value, then is declared nonestimable. The matrix is the Hermite form matrix , where represents a generalized inverse of the information matrix of the null model. The value must be between 0 and 1; the default is .